Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long Tail Effect. A large number of students who have sparse data are performed poorly in the model. To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist the graph-based cognitive diagnosis, then the performance on those students with sparse data can be improved. Specifically, we came up with a graph confusion method that drops edges under some special rules to generate different sparse views of the graph. By maximizing the consistency of the representation on the same node under different views, the model could be more focused on long-tailed students. Additionally, we proposed an importance-based view generation rule to improve the influence of long-tailed students. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with sparse data.
Code (0)
등록된 구현이 없습니다.
Tasks
cognitive diagnosisGraph LearningSimilar Papers 제목 키워드 기반
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution. However, practical test class …
Image ClassificationLong-tail LearningTest Agnostic Long-Tailed LearningFrequency-Aware Self-Supervised Long-Tailed Learning
Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples. While existing supervised learning appro…
Self-Supervised LearningEEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning
Cognitive load, the amount of mental effort required for task completion, plays an important role in performance and decision-making outcomes, making its classification and analysis essential in various sensitive domains…
ClassificationDecision MakingEEGElectroencephalogram (EEG)+1Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning
Self-supervised learning (SSL) has shown remarkable data representation capabilities across a wide range of datasets. However, when applied to real-world datasets with long-tailed distributions, performance on multiple d…
Contrastive LearningPseudo LabelSelf-Supervised LearningDistantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs
Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tail…
DenoisingRelationRelation ExtractionRelationship Extraction (Distant Supervised)+1